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-augmented generation (RAG) or agent-based AI systems. Experience working with APIs, data pipelines, or backend systems. Experience evaluating or benchmarking ML or LLM-based systems. Knowledge of FAIR
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Routers/Switches including Catalyst, Nexus, MX, EX, SRX. Knowledge of Python, REST-API, Shell Scripting and other scripting languages highly desired. Ability to handle constantly changing work priorities
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repositories (e.g., catalogs, APIs, and bulk downloads), including provenance tracking, metadata extraction, and licensing/usage notes. Build scalable geospatial data preparation and validation routines
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stakeholders to capture needs and translate them into system and software design. Architect, develop and integrate full-stack solutions: front-end (UI/UX), middleware/API, backend services, databases
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(RAG) systems for scientific applications Build programmatic APIs and natural language interfaces that enable researchers to query, retrieve, and analyze Populus genomic data and associated microbial
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, documenting, and consuming APIs. On the back end, you have hands‑on experience with Python, Django, and Django REST Framework to design reliable, secure RESTful services. You write effective unit tests and test
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: Experience with SDR software frameworks/APIs such as GNU Radio, UHD, SoapySDR, libsidekiq, etc. Experience with SDR hardware from vendors such as Ettus, Epiq, etc. Printed circuit board (PCB) design and
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requirements; work with stakeholders to capture needs and translate them into system and software design. Architect, develop and integrate full-stack solutions: front-end (UI/UX), middleware/API, backend
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and platforms via APIs Experience working with ticketing systems such as Service Now and Atlassian products Experience with Azure or other cloud technologies Experience supporting DOE facilities
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of scientific AI. Focus Areas: Cross-Domain Interoperability: Develop common readiness templates, standardized metadata models, and APIs to enable seamless integration across diverse scientific domains